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All Journal GEMA TEKNOLOGI JURNAL SISTEM INFORMASI BISNIS JIK Jurnal Ilmu Komputer Emitor: Jurnal Teknik Elektro JTET (Jurnal Teknik Elektro Terapan) E-Dimas: Jurnal Pengabdian kepada Masyarakat Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal SOLMA JMM (Jurnal Masyarakat Mandiri) JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Jurnal Penelitian dan Pengabdian Kepada Masyarakat UNSIQ KOMPUTIKA - Jurnal Sistem Komputer KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Pilar Teknologi : Jurnal Penelitian Ilmu-ilmu Teknik Journal of Soft Computing Exploration Jurnal Informatika: Jurnal Pengembangan IT

Dega Surono Wibowo, Dega Surono

Politeknik Harapan Bersama

Author-ID : 517149

Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Mechanical Engineering Public Health Social Sciences Other

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Implementasi Metode K-Means berbasis Chi-Square pada Sistem Pendukung Keputusan untuk Identifikasi Disparitas Kebutuhan Guru Nishom, M.; Wibowo, Dega Surono
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 8, No 2 (2018): Volume 8 Nomor 2 Tahun 2018
Publisher : Universitas Diponegoro

In this research, the Chi-Square-based K-Means method was implemented in the Decision Support System (DSS) to identify the disparity in Teacher's needs compared to the real conditions of Teacher's availability in the education unit (school). This is very important, because based on data from the UNESCO Institute for Statistics shows that the ratio between teachers and students in Indonesia is the lowest in the world. This is influenced by the distribution of Teachers who do not meet the needs and exceed the number of student enrollments at all levels of education, resulting in less optimal quality of education produced in various regions in Indonesia. Thus, it is necessary to group data and label the disparity of Teacher's needs in educational units in various regions in Indonesia, especially in the Tegal City. In this case, the K-Means Clustering method was used to group data based on Teacher's availability data, and Chi-Square analysis was used to determine the disparity in Teacher's needs with the condition of Teacher's availability. Data collection methods used in research are observation methods. The results showed that the DSS application that had been produced could dynamically determine the education unit cluster based on the teacher availability disparity category in the Tegal City. In addition, labeling of the K-Means cluster based on the Chi-square test has a high degree of accuracy, which is 84.47%.

Diagnosis Kerusakan Bantalan Gelinding Pada Sistem Industri Dengan Metode Self Organizing Map (SOM) Wibowo, Dega Surono; Widodo, Achmad
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 4, No 1 (2014): Volume 4 Nomor 1 Tahun 2014
Publisher : Universitas Diponegoro

This research is discussing about the usage of data mining which addressed to damage diagnosis of rolling bearings. Data input was obtained from signal frequency feature extraction which taken from calibration against rolling bearings. The diagnosis was extremely important to industrial machines since this diagnosis can help to discover damages that occurred so that total failure of cessation of the machines can be avoided and industrial machines treatment costs can be optimized. Method used in this research is Self Organizing Map (SOM), SOM method on this research was done by sequence: signal frequency data that have been through the process of acquisition and preprocessing, feature extraction, Principal Component Analysis (PCA), then come into the process of SOM so that accuracy of the diagnosis process can be discovered. The result of this research is a software that can diagnose rolling bearings damage on industrial system. From tests result, software that has been produced was able to diagnose rolling bearings damage. Accuracy result shown 87.5% success, this software can be developed further to help technicians in diagnosing rolling bearings damage. This research method can be developed further to detect other damages in industrial systems. Â Keywords: Data mining; PCA; SOM; Diagnosis; Rolling bearings; Statistic feature extraction

Co-Authors Achmad Widodo Ardi Susanto Ardi, Ardi Susanto Bachtiar, M. Yusuf Dairoh Dairoh, Dairoh Erinsyah, Moh Fiqih Erinsyah, Moh. Fiqih Faulin, Muhammad Husni Ginanjar Wiro Sasmito, Ginanjar Wiro Hepatika Zidny Ilmadina Insani, Fariq Fadillah Gusti Karenina, Vita Krisnawati, Eli M Yoka Fathoni Miftakhul Huda Muhammad Fikri Hidayattullah, Muhammad Fikri Nishom, M Oman Somantri riszki wijayatun pratiwi, riszki wijayatun Sena Wijayanto Syamsiar, Syamsiar Taufiq Abidin, Taufiq Very Kurnia Bakti, Very Kurnia W, M Shidiq Erdi Wiyono, Slamet Yustia Hapsari, Yustia

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